dev:dry-run

Smoke-test the evolve pipeline for tool syntax, argparse behavior, and end-to-end execution.

43|5|Updated Mar 31, 2026
One-click install
npx skills add https://github.com/raphaelchristi/harness-evolver --skill dev-dry-run
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dev:dry-run
Source: https://github.com/raphaelchristi/harness-evolver/tree/main/.claude/skills/dev-dry-run
Command: npx skills add https://github.com/raphaelchristi/harness-evolver --skill dev-dry-run

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a safe, repeatable smoke test for the Harness Evolver optimization pipeline to verify that tooling, CLI flags, and the evaluation flow are configured correctly before running full experiments.

Core Features & Use Cases

  • Offline validation: Checks Python syntax, argparse help output, and skill-to-tool cross-references to catch integration issues without external services.
  • Online mock evaluation: When LANGSMITH_API_KEY is present, runs a mock agent through setup, evaluation, results reading, and insight tracing to validate the end-to-end pipeline.
  • Use Case: Run this skill after installing or changing tools to ensure setup.py, run_eval.py, read_results.py, and trace_insights.py behave as expected and produce readable outputs.

Quick Start

Run the dev:dry-run skill to smoke-test the evolve pipeline, validating tool syntax, argparse behavior, cross-references, and optional online evaluation when LANGSMITH_API_KEY is set.

Frequently Asked Questions about dev:dry-run

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I smoke-test an evolve pipeline before running full experiments?▼

To smoke-test an evolve pipeline, run a dry-run validation that checks Python tool syntax, argparse behavior, and end-to-end execution of setup, evaluation, and result tracing to catch integration issues safely before full experiments.

What does offline pipeline validation check for without external services?▼

Offline pipeline validation checks Python syntax, argparse help output, and skill-to-tool cross-references to catch integration issues without requiring external services or API connections.

Do I need a LangSmith API key to run an end-to-end mock evaluation?▼

You need a LANGSMITH_API_KEY to run an end-to-end mock evaluation. When present, it triggers a mock agent through setup, run_eval, read_results, and trace_insights steps to validate the online pipeline.

How do I validate argparse behavior and tool syntax for Python scripts in CI?▼

You can validate argparse behavior and tool syntax in CI by running a dry-run smoke test that uses EVOLVER_TOOLS and EVOLVER_PY environment variables to locate and execute Python-based tool scripts for automated validation.

When should I run a dry-run smoke test on my optimization pipeline?▼

You should run a dry-run smoke test after installing or changing tools to ensure setup.py, run_eval.py, read_results.py, and trace_insights.py behave as expected and produce readable outputs before executing full optimization runs.